Principal GenAI & Agentic AI Engineer

Aimlroles; Bengaluru; Sur place; INR 4 000 000 - 6 000 000; Plein temps

Résumé du poste

Sabre seeks a Principal GenAI & Agentic AI Engineer to design, build, and scale AI systems on Google Cloud. You will define architectures for GenAI apps, lead multi-team delivery, and govern safety, reliability, and cost at enterprise scale to monetize AI-based products.

You will mentor engineers, drive data pipelines, and establish reusable patterns and guardrails across the organization, partnering with product, security, and SRE teams.

Qualifications

• 12-15+ years in software/data/ML engineering; 1+ years hands-on with LLMs/GenAI and agentic systems.
• Proven delivery of enterprise-scale GenAI/agent platforms on GCP (Vertex AI, BigQuery, Cloud Run, Pub/Sub, Workflows).
• Strong proficiency in Java and Python/TypeScript, with IaC (Terraform).
• Experience in security-by-design, privacy, and compliance audits.
• Proven data pipeline development using Dataflow/Apache Beam and distributed computing.

Responsabilités

• Define reference architectures for GenAI apps, RAG systems, and agent ecosystems on GCP with ADK.
• Lead multi-team delivery, focusing on reliability, safety, and cost control.
• Design production-grade GenAI solutions and agent services (planning, tools, memory, HIL).
• Drive data pipelines (ingestion, indexing, events) and secure API exposure patterns.
• Standardize LLMOps: CI/CD, model registry, tracing, and cost/performance scorecards.
• Mentor engineers, run architecture reviews, and publish quality/security playbooks.

Connaissances

GenAI

LLM

GCP

Data pipelines

Security

Observability

Cost optimization

Cloud architecture

Java

Python/TypeScript

Outils

Vertex AI

BigQuery

Cloud Run

Pub/Sub

Workflows

Dataflow/Apache Beam

Terraform

Okta/IAP

Apigee

Description du poste

Powering the agentic revolution in travel. Sabre is an AI-native technology leader, backed by one of the world’s largest travel data clouds. Built on an open, modular, cloud-native architecture, Sabre serves as the backbone for both established leaders and bold, new disruptors, guiding them to the next age of travel retailing through intelligent, connected, and personalized experiences. With AI at its core and operating at unparalleled scale, Sabre transforms insights into innovation, empowering airlines, hoteliers, agencies and other partners to retail, distribute and fulfill travel worldwide.

The Principal GenAI & Agentic AI Engineer is the technical leader responsible for designing, building, and scaling AI systems that combine LLM-powered GenAI and ADK-based agentic workflows on Google Cloud Platform. This role also requires leading and developing data pipelines for necessary data layer for AI/ML. This role sets architecture standards, leads multi-team delivery, and governs safety, reliability, and cost at enterprise scale—accelerating product teams to achieve monetization of AI based products through reusable patterns, platforms, and guardrails.

Key Responsibilities

Strategy & Architecture

• Define reference architectures for GenAI apps, RAG systems, and agent ecosystems (single/multi-agent) on GCP using ADK.

• Leverage capabilities of Gemini Enterprise Agent Platform in the Agentic AI Product development.

• Establish domain and platform standards: model selection, RAG/generation patterns, memory architectures, security baselines, observability, and LLMOps.

• Lead portfolio-wide technical decisions (build/buy, vendor selection, SLAs, quotas) with a focus on reliability, safety, and cost control.

• Define the data pipeline development for lakehouse, delta lake or feature engineering.

Solution Design & Delivery

• Architect and lead implementation of production-grade GenAI solutions (Vertex AI models, Grounding, Pipelines, Evaluation) and agentic services (planning, tools, memory, HIL).

• Design multi-tenant and hub-and-spoke patterns with Okta/IAP/Apigee for secure API exposure and tenant isolation.

• Drive end-to-end delivery across teams: data ingestion (Dataflow/Composer), indexing (BigQuery vectors/Vertex Vector Search), services (Cloud Run/Workflows), events (Pub/Sub).

• Data Pipeline both near real time and batch.

Platformization & Reuse

• Build and maintain prompt libraries, tool catalogs, agent templates, and evaluation harnesses for organization-wide reuse.

• Standardize LLMOps: CI/CD for prompts/models/agents, model registry, traceability, rollback, canaries, cost/performance scorecards.

• Enable a marketplace of agents/services with productized APIs, documentation, chargeback, and KPIs.

Responsible AI, Security & Compliance

• Implement multi-layer guardrails: policy prompts, filters, memory governance, tool whitelisting, audit logs; ensure regulator-ready posture.

• Codify privacy, PII handling, data residency, and per-tenant isolation using VPC-SC, Secret Manager, IAM, and Apigee policies.

Leadership & Enablement

• Mentor senior engineers and team leads; run architecture reviews, design clinics, and red-team exercises.

• Drive continuous evaluation programs and publish org scorecards for quality, safety, and cost.

• Partner with Product, Security, and SRE to align roadmaps, SLOs, and operational playbooks.

Required Technical Competencies

• Dataflow and Apache Beam for data pipeline development.

• Strong on using SQL for data analysis.

• LLM & GenAI: Model selection (Gemini & Model Garden), prompt engineering, RAG/grounding, multimodal pipelines, fine-tuning/adapter methods.

• Agentic AI (ADK): Agent loops, planners, tool/function design, memory (episodic/semantic/long-term), HIL, policy enforcement.

• Data & Retrieval: BigQuery (including vector functions), Vertex Vector Search, Document AI, Dataplex for lineage and governance.

• Orchestration & Services: Cloud Run, Workflows, Pub/Sub, Dataflow/Composer; HA/DR, backpressure, circuit breakers.

• LLMOps/MLOps: Vertex AI Pipelines, registry, CI/CD, trace correlation, cost/performance monitoring.

• Security & Compliance: IAM, Secret Manager, VPC-SC, private service connect, DLP, Okta/IAP, Apigee API policies.

• Observability & Cost: Central telemetry, user feedback loops, drift/outlier detection, quota/capacity planning.

Qualifications

• 12-15+ years in software/data/ML engineering; 1+ years hands-on with LLMs/GenAI and agentic systems.

• Proven delivery of enterprise-scale GenAI/agent platforms on GCP (Vertex AI, BigQuery, Cloud Run, Pub/Sub, Workflows).

• Demonstrated impact in platformization, governance, and multi-team technical leadership.

• Strong proficiency in Java.

• Strong proficiency in Python/TypeScript (or equivalent) and infrastructure-as-code (Terraform/GCP Deployment Manager).

• Experience in security-by-design, privacy, and compliance audits.

• Proven delivery in building data pipeline using distributed computing frameworks such as Dataflow, Spark.